Event-Driven Microservices Architecture

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📚 Documentation Overview

I’ve created 3 comprehensive guides for implementing Kafka in your Modern Reservation System:

1. KAFKA_IMPLEMENTATION_GUIDE.md (Main Guide - 1000+ lines)

• Complete architectural overview
• Detailed event schema design
• Phase-by-phase implementation (6 weeks)
• Code examples for all patterns
• Best practices and security
• Use this for: Understanding the big picture

2. event-driven-architecture-diagram.md (Visual Guide)

• Visual architecture diagrams
• Event flow illustrations
• Before/After comparisons
• Service communication matrix
• Scalability models
• Use this for: Understanding system design visually

3. KAFKA_QUICK_START.md (Practical Guide)

• Step-by-step implementation
• Day-by-day breakdown (8 days)
• Copy-paste code snippets
• Testing procedures
• Troubleshooting tips
• Use this for: Actually implementing the system

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🎯 Big Picture Summary

Current State

Your services use:

• ✅ Spring Boot microservices
• ✅ Eureka for service discovery
• ✅ PostgreSQL for data persistence
• ✅ Redis for caching
• ⚠️ HTTP/REST for inter-service communication (if any)
• ⚠️ Kafka configured but not implemented

Target State

Event-driven architecture with:

• ✅ Kafka as event backbone
• ✅ Asynchronous communication
• ✅ Loose coupling between services
• ✅ Complete audit trail
• ✅ Scalable and resilient

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🏗️ Architecture Transformation

Before (Synchronous)

Guest → API Gateway → Reservation Engine
                            ├─→ HTTP call → Availability Calculator
                            ├─→ HTTP call → Payment Processor
                            ├─→ HTTP call → Rate Management
                            └─→ HTTP call → Analytics Engine

Problems:

• ❌ Tight coupling
• ❌ Cascading failures
• ❌ Slow (sequential processing)
• ❌ Difficult to scale

After (Event-Driven)

Guest → API Gateway → Reservation Engine
                            │
                            ↓ Publish: reservation.created
                      ┌─────┴─────┐
                      │   Kafka   │
                      └─────┬─────┘
                ┌───────────┼───────────┬───────────┐
                ↓           ↓           ↓           ↓
        Availability    Payment      Rate      Analytics
        Calculator     Processor  Management    Engine

Benefits:

• ✅ Loose coupling
• ✅ Independent services
• ✅ Fast (parallel processing)
• ✅ Easy to scale
• ✅ Complete event history

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📦 Key Components

1. Event Types (Topics)

reservation.*     → Booking lifecycle events
availability.*    → Inventory management events
payment.*         → Financial transaction events
rate.*            → Pricing and rate events
analytics.*       → Business intelligence events

2. Services as Producers

Reservation Engine  → Publishes: reservation.created, reservation.cancelled
Payment Processor   → Publishes: payment.authorized, payment.captured
Availability Calc   → Publishes: availability.updated
Rate Management     → Publishes: rate.updated

3. Services as Consumers

Payment Processor   → Consumes: reservation.created
Availability Calc   → Consumes: reservation.created, reservation.cancelled
Rate Management     → Consumes: availability.updated
Analytics Engine    → Consumes: ALL events

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🔄 Example Flow: Create Reservation

Step-by-Step

1. Guest submits booking via API
2. Reservation Engine:
   • Validates request
   • Saves to database
   • Publishes reservation.created event
   • Returns confirmation immediately
3. Kafka distributes event to all consumers
4. Parallel processing (all happen simultaneously):
   • Availability Calculator: Reduces inventory
   • Payment Processor: Initiates payment
   • Rate Management: Tracks rate usage
   • Analytics Engine: Updates dashboards
5. Each service publishes its own events
6. System converges to final state

Performance

• Before (HTTP): ~2000ms (sequential)
• After (Kafka): ~500ms (parallel)
• Improvement: 4x faster

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🛠️ Implementation Path

Week 1: Infrastructure

• Add Kafka to Docker
• Add Kafka UI for monitoring
• Test connectivity
• Update scripts

Week 2: Shared Library

• Create event models
• Create event publisher
• Build shared library
• Write unit tests

Week 3: Producer

• Update Reservation Engine
• Publish reservation.created
• Test event publishing
• Verify in Kafka UI

Week 4: Consumers

• Create Payment Processor consumer
• Create Availability Calculator consumer
• Create Analytics Engine consumer
• Test end-to-end flow

Week 5-6: Testing & Optimization

• Integration testing
• Performance testing
• Monitor consumer lag
• Tune configurations

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📊 Expected Outcomes

Scalability

• Current: 100 reservations/minute
• Target: 10,000 reservations/minute
• Method: Add more Kafka partitions + service instances

Reliability

• Before: If one service down → entire flow fails
• After: Services work independently, events queued

Maintainability

• Before: Change requires coordinating multiple services
• After: Add new consumer without touching producers

Observability

• Before: Scattered logs across services
• After: Complete event stream in Kafka

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🎓 Key Concepts

Event Sourcing

• All changes stored as immutable events
• Can replay events to rebuild state
• Complete audit trail

Event Choreography

• Services react to events independently
• No central orchestrator
• Loose coupling

Consumer Groups

• Multiple consumers share workload
• Each consumer gets subset of messages
• Scales horizontally

Idempotency

• Processing same event multiple times = same result
• Critical for reliability
• Handle duplicates gracefully

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🚀 Quick Start Commands

# 1. Start Kafka
cd /home/subramani/modern-reservation
bash infra.sh start

# 2. Access Kafka UI
# Browser: http://localhost:8090

# 3. Build shared library
cd libs/shared/backend-utils
mvn clean install

# 4. Rebuild services
cd apps/backend/java-services
mvn clean install

# 5. Test
curl -X POST http://localhost:8080/reservation-engine/api/v1/reservations \
  -H "Content-Type: application/json" -d '{...}'

# 6. Check Kafka UI for events

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📚 Reference Documents

Document	Purpose	When to Use
KAFKA_IMPLEMENTATION_GUIDE.md	Complete reference	Understanding architecture
event-driven-architecture-diagram.md	Visual diagrams	Design discussions
KAFKA_QUICK_START.md	Practical steps	Actual implementation

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🎯 Success Criteria

✅ Kafka running in Docker
✅ Kafka UI accessible at http://localhost:8090
✅ Shared event library built and published
✅ Reservation Engine publishes events
✅ Consumers receive and process events
✅ End-to-end flow tested
✅ Logs show event publishing/consuming
✅ Kafka UI shows messages

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🔍 Monitoring URLs

• Kafka UI: http://localhost:8090
• Eureka Dashboard: http://localhost:8761
• Gateway: http://localhost:8080
• Zipkin Tracing: http://localhost:9411

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💡 Pro Tips

1. Start small: Implement one event type first (reservation.created)
2. Use Kafka UI: Visual monitoring is essential for debugging
3. Log everything: Use structured logging for event tracking
4. Test failure scenarios: Kill services and verify events are queued
5. Monitor consumer lag: Alert if lag exceeds threshold
6. Use correlation IDs: Track events across services
7. Implement idempotency: Handle duplicate messages gracefully

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🤝 Next Steps

1. Read: Start with KAFKA_QUICK_START.md
2. Setup: Follow Phase 1 (Kafka infrastructure)
3. Build: Create shared event library
4. Implement: Start with Reservation Engine producer
5. Test: Verify events in Kafka UI
6. Expand: Add more events and consumers iteratively

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Document Version: 1.0
Last Updated: October 6, 2025
Status: Ready for Implementation

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📞 Questions?

Review the detailed guides:

• Architecture questions → KAFKA_IMPLEMENTATION_GUIDE.md
• Visual understanding → event-driven-architecture-diagram.md
• Implementation steps → KAFKA_QUICK_START.md